#for each alpha level, this function computes the power of each type simulated statistics
#across each model type
"compute.empirical.powers" = function(statistics.matrix, alpha.levels){
	matrix.columns = dimnames(statistics.matrix)[[2]]
	simulation.runs = length(dimnames(statistics.matrix)[[1]])
	sample.point = as.data.frame(statistics.matrix[1,1][[1]])
	statistics.names.column.nums = grep("name",names(sample.point))
	statistics.column.nums = grep("STATISTIC",names(sample.point))
	lags.column.nums =  grep("lags",names(sample.point))
	pval.column.nums =  grep("PVAL",names(sample.point))
	df.column.nums =  grep("df",names(sample.point))
	number.of.stats = length(statistics.names.column.nums)
	for (i in 1:length(matrix.columns)){
			test.failed.aggregator = NULL
		for(j in 1:simulation.runs){

			sample.point = as.data.frame(statistics.matrix[i,j][[1]])
			pass.fail = compute.pass.fail(sample.point)
			}	
		}
	}
	list.item	
	return(structure(list()));
}